Interference Diversity Gain and its Application in Multi-Channel Systems: Capacity Maximization and QoS Guarantee Strategies
Bibliographic record
Abstract
As the spectral efficiency of wireless communications systems increases, and the frequency reuse pattern shifts towards universal frequency reuse, the capacity of future wireless and mobile systems will becomes interference limited. We explore the interference pattern of a shared channel in order to establish optimal distributed resource allocation techniques in that channel considering the mutual interference of coexisting links on each other. It is shown that the optimal usage of resources in such a shared channel can only be achieved via a multi-dimensional resource allocation strategy, taking into account not only the link's own channel quality, but also its channel states towards coexisting links. The improvement of capacity as a result of this approach can be attributed to the interference diversity of the channel. The interference diversity gain can be harnessed in time, frequency and space domains. To this end we study two approaches, namely maximizing the capacity of the primary and secondary links under received interference constraint and minimizing the transmitted power of primary and secondary links under minimum QoS guarantee constraint. We study the Ergodic capacity to verify the significant performance improvement which can be achieved by exploiting interference diversity in multi-channel systems such as UMTS Long Term Evolution (LTE). Further we will show that utilizing this diversity gain in QoS-guaranteed scenarios results in a considerable transmission power saving. The case of Outage capacity, which is an instantaneous measure of channel throughput, is shown to be different whereby using an instantaneous received interference threshold outperforms the usage of average received interference limit.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".